Ripple-spikes localize the epileptogenic zone and its interpretation: integrating data-driven and model-driven methods

First, this study evaluates whether ripples co-occurring with epileptiform spikes (i.e., ripple-spikes) in scalp EEG provide a reliable biomarker for epileptogenic zone (EZ) localization compared to ripples or spikes alone. Then, this study develops a novel computational hippocampo-cortical model to explore the mechanisms underlying ripple-spikes and provides a mechanistic explanation for why ripple-spikes locate EZ more effectively than ripples or spikes. We apply automated detectors to identify ripples, spikes, and ripple-spikes on scalp EEG recordings in patients with focal epilepsy from two public datasets, and further perform a statistical analysis of the rates of these three events in EZ and non-epileptogenic zone (NEZ); the model consists of a hippocampal CA1 module and a cortical module, where an electrical synaptic current is introduced in the CA1 module and the reducing of GABAergic inhibition is modeled in the cortical module. Real data analysis results show a significantly higher ratio of ripple-spike rate in EZ to that in NEZ, compared to ripple ($p=0.01$) or spike ($p=0.008$); simulation results demonstrate that: 1) ripple-spikes can be mediated by the hippocampo-cortical connection strength; 2) and such connection may be stronger for EZ than that for NEZ, leading to a higher ratio of ripple-spike rate. Ripple-spikes in scalp EEG may serve as an improved biomarker for EZ identification, potentially explained by stronger hippocampo-cortical connection for EZ. Providing a meaningful attempt in terms of the computational method to studying ripple-spikes, offering both data and theoretical support for advancing noninvasive methods of EZ localization.